Change detection in diffusion MRI using multivariate statistical testing on tensors

Authors:
Antoine Grigis;Vincent Noblet;Félix Renard;Fabrice Heitz;Jean-Paul Armspach;Lucien Rumbach
Affiliations:
University of Strasbourg, CNRS, UMR, LSIIT, France and University of Strasbourg, CNRS, FRE, LINC-IPB, France;University of Strasbourg, CNRS, UMR, LSIIT, France;University of Strasbourg, CNRS, UMR, LSIIT, France and University of Strasbourg, CNRS, FRE, LINC-IPB, France;University of Strasbourg, CNRS, UMR, LSIIT, France;University of Strasbourg, CNRS, FRE, LINC-IPB, France;University of Strasbourg, CNRS, FRE, LINC-IPB, France
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Year:
2010

Citing 4
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Abstract

This paper presents a longitudinal change detection framework for detecting relevant modifications in diffusion MRI, with application to Multiple Sclerosis (MS). The proposed method is based on multivariate statistical testings which were initially introduced for tensor population comparison. We use these methods in the context of longitudinal change detection by considering several strategies to build sets of tensors characterizing the variability of each voxel. These testing tools have been considered either for the comparison of tensor eigenvalues or eigenvectors, thus enabling to differentiate orientation and diffusivity changes. Results on simulated MS lesion evolutions and on real data are presented. Interestingly, experiments on an MS patient highlight the ability of the proposed approach to detect changes in non evolving lesions (according to conventional MRI) and around lesions (in the normal appearing white matter), which might open promising perspectives for the follow-up of the MS pathology.